AI-powered surveillance detects people and vehicles, tracks them across cameras, evaluates zone and behavior rules in real time and delivers verified events to a central monitoring view.
Security teams are responsible for more cameras than they can watch. Footage is typically reviewed only after something has gone wrong, and conventional motion detection fires on shadows, headlights, animals and weather until operators stop trusting alerts altogether.
Meanwhile the questions that matter stay unanswered in real time: is someone in a restricted area after hours, is a crowd building at an exit, did a vehicle enter through a closed gate, has the same person appeared at three entrances in ten minutes?
Our surveillance solution replaces pixel-motion alerts with object-aware intelligence. Detection and tracking identify what is in the scene, configurable rules decide what matters and operators receive events with evidence attached, across the whole estate from one console.
Each capability is marked so you can distinguish what we deliver today from capabilities engineered to your specification.
Deep learning detectors locate people in day, night and low-light conditions, distinguishing them from vehicles, animals and environmental motion.
Each detected person or vehicle receives a persistent identity and trajectory within a camera view, surviving brief occlusion and crowding.
Line-crossing and zone-entry rules on tracked trajectories, with schedules and direction constraints, so an alert means a person actually entered.
Density estimation and occupancy counts for areas where individuals cannot be tracked separately, with threshold alerts for crowding.
Polygon zones with time-based rules flag presence, dwell and loitering in sensitive areas such as server rooms, loading bays and perimeters.
Deduplicated, severity-scored events with clips and snapshots delivered to dashboards, mobile notifications, email and webhooks.
A single console aggregates live status, events and evidence from every camera and site, with role-based access for operators and managers.
Appearance embeddings link the same person or vehicle across non-overlapping cameras to reconstruct movement through a site.
Events pushed into existing video management systems and correlated with badge, door and alarm data.
The pipeline behind the solution, stage by stage.
RTSP and ONVIF streams from existing or new cameras are ingested by edge devices or an on-premise server. No camera replacement is required.
Optimized detectors classify people and vehicles in every stream; a multi-object tracker assigns identities and builds trajectories.
Operators draw zones and lines and set schedules. Rules run on trajectories, not raw motion, so an alert reflects a real object doing a real thing.
Events are deduplicated, scored and packaged with video evidence, then delivered to the monitoring console and any connected systems.
Operator feedback on each alert tunes rules and thresholds, steadily lowering false alarms as the system learns the site.
Operators act on verified events instead of monitoring screens.
False alarms from weather, animals and lighting are substantially filtered out.
Incidents are detected while they are happening rather than discovered afterwards.
Evidence is attached to every event, shortening investigation and reporting.
Existing camera investments gain intelligence without replacement.
Real-time video understanding, event detection, behavioral analysis and multi-camera analytics.
Custom detection, classification, segmentation, recognition and tracking systems.
Deploy optimized AI models directly on edge hardware for responsive local inference.
Tell us about your site and your current monitoring setup. We will map which events can be detected and how the system would fit your estate.